This course will help you to expertise the usage of Python in Data Science world.
Carter your Python Knowledge so that it can be utilized to get the Insights of Data using Methodologies and Techniques of Data Science...
Objective:
Understand the concepts of Data science and Python
You will be able to use Python in Discovering Data.
You will have an idea of Statistical and Analytical methods to deal with huge data sets.
You will gain an expertise on Regular Expressions, looping functions and concepts of Object Oriented Programming.
You will be able to create business algorithms and data models using Python and it's techniques.
Work on Real-life Projects will help you to get a practical experience of real scenarios of IT Industry.
Start learning Python for Data Science from basics to advance levels here...
https://goo.gl/070wXw
I have recently started my 4th self-published book, ReportLab: PDF
Processing with Python. This is the first book on ReportLab, a great
package for creating reports in the PDF format. You can read more about it
here:
https://www.kickstarter.com/projects/34257246/reportlab-pdf-processing-with…
Thanks,
Mike
The pytest team is proud to announce the 3.4.0 release!
pytest is a mature Python testing tool with more than a 1600 tests
against itself, passing on many different interpreters and platforms.
This release contains considerable improvements to the logging facilities
as well as other fixes and improvements, so users are encouraged to take a
look at the CHANGELOG:
http://doc.pytest.org/en/latest/changelog.html
For complete documentation, please visit:
http://docs.pytest.org
As usual, you can upgrade from pypi via:
pip install -U pytest
Thanks to all who contributed to this release, among them:
* Aaron
* Alan Velasco
* Anders Hovmöller
* Andrew Toolan
* Anthony Sottile
* Aron Coyle
* Brian Maissy
* Bruno Oliveira
* Cyrus Maden
* Florian Bruhin
* Henk-Jaap Wagenaar
* Ian Lesperance
* Jon Dufresne
* Jurko Gospodnetić
* Kate
* Kimberly
* Per A. Brodtkorb
* Pierre-Alexandre Fonta
* Raphael Castaneda
* Ronny Pfannschmidt
* ST John
* Segev Finer
* Thomas Hisch
* Tzu-ping Chung
* feuillemorte
Happy testing,
The Pytest Development Team
Hi everyone,
I'd like to announce new releases of tex2ipy and ipyaml on pypi.
tex2ipy: https://github.com/prabhuramachandran/tex2ipy
This simple tool makes it relatively easy to convert a LaTeX beamer presentation
to a Jupyter/IPython notebook using RISE[1]. This version fixes some issues,
works with the latest version of TexSoup and nbformat.
The second tool is:
ipyaml: https://github.com/prabhuramachandran/ipyaml
This allows Jupyter to store notebook files as easy-to-edit YAML files. It
supports both code/markdown and outputs so is entirely compatible with .ipynb
files (version 4 of the notebook format). Output dumping can be disabled if
needed. This package is similar to notedown and ipymd but offers complete
compatibility with .ipynb files. This version works with the latest nbformat
version and also reduces a little indentation to make the yaml files a little
easier on the eye.
cheers,
Prabhu
[1] https://github.com/damianavila/RISE
p.s. sorry about the cross posting.
As Ned just announced, Python 3.7 is very soon to enter beta 1 and thus feature freeze. I think we can all give Ned a huge round of applause for his amazing work as Release Manager for Python 3.6 and 3.7. Let’s also give him all the support he needs to make 3.7 the best version yet.
As is tradition, Python release managers serve for two consecutive releases, and so with the 3.7 release branch about to be made, it’s time to announce our release manager for Python 3.8 and 3.9.
By unanimous and enthusiastic consent from the Python Secret Underground (PSU, which emphatically does not exist), the Python Cabal of Former and Current Release Managers, Cardinal Ximénez, and of course the BDFL, please welcome your next release manager…
Łukasz Langa!
And also, happy 24th anniversary to Guido’s Python 1.0.0 announcement[1]. It’s been a fun and incredible ride, and I firmly believe that Python’s best days are ahead of us.
Enjoy,
-Barry
[1] https://groups.google.com/forum/?hl=en#!original/comp.lang.misc/_QUzdEGFwCo…
On behalf of the Python development community, I'm pleased to announce
the availability of Python 3.4.8rc1 and Python 3.5.5rc1.
Both Python 3.4 and 3.5 are in "security fixes only" mode. Both versions
only accept security fixes, not conventional bug fixes, and both
releases are source-only.
You can find Python 3.4.8rc1 here:
https://www.python.org/downloads/release/python-348rc1/
And you can find Python 3.5.5rc1 here:
https://www.python.org/downloads/release/python-355rc1/
Happy Pythoning,
//arry/
Hi everyone,
I'd like to announce new releases of tex2ipy and ipyaml on pypi.
tex2ipy: https://github.com/prabhuramachandran/tex2ipy
This simple tool makes it relatively easy to convert a LaTeX beamer presentation
to a Jupyter/IPython notebook using RISE[1]. This version fixes some issues,
works with the latest version of TexSoup and nbformat.
The second tool is:
ipyaml: https://github.com/prabhuramachandran/ipyaml
This allows Jupyter to store notebook files as easy-to-edit YAML files. It
supports both code/markdown and outputs so is entirely compatible with .ipynb
files (version 4 of the notebook format). Output dumping can be disabled if
needed. This package is similar to notedown and ipymd but offers complete
compatibility with .ipynb files. This version works with the latest nbformat
version and also reduces a little indentation to make the yaml files a little
easier on the eye.
cheers,
Prabhu
[1] https://github.com/damianavila/RISE
Hello,
this is the first release (beta) of two Python modules for describing and
manipulating unboxed in-memory data.
ndtypes
=======
ndtypes is based on the libndtypes C-library. libndtypes has four main
purposes:
1) Typing a memory block (abstract type).
2) Storing the exact layout information of a memory block (concrete type).
3) Constructing the type from a close variant of the datashape type language.
3) Dynamic type checking of memory blocks by pattern-matching an abstract
function signature against the types of concrete input arguments.
ndtypes implements all the heavy-lifting for typing and handling memory blocks
and is meant to be used as a foundation for other modules.
xnd
===
xnd is based on the libxnd C-library and the ndtypes module. It defines and
manages typed memory blocks. Because libndtypes has full layout information,
the xnd memory block is a small C-struct that contains bitmaps for optional
data, a linear index for addressing bitmaps, the type and the data pointer.
This makes it easy to implement lightweight memory views that support the
whole range of C types relevant for scientific computing.
Since libndtypes uses a standard algebraic data type to store recursive
type and layout information, mapping and unpacking nested Python values
directly to memory is straightforward.
Unless the type contains explicit pointer types like references, bytes or
strings, a single, pointer-free memory block is allocated.
List of xnd's features
----------------------
1) Missing values (NA) are supported for all data. The bitmaps are
Arrow compatible.
2) ndarrays with arbitrarily nested element types and optional values.
3) Ragged arrays using offset addressing with nested element types and
optional values. The ragged array type is compatible with the Arrow
list type.
4) Slicing and multi-dimensional indexing. Slicing and indexing returns
fully typed views on the data.
5) First class tuples and records with detailed explicit alignment
and packing.
6) Constructor types (Joule(float32) != Ampere(float32)).
7) Reference types for compatibility with other memory block libraries.
8) Buffer protocol support for importing other memory blocks while
translating PEP-3118 format strings to datashape.
Links
=====
https://github.com/plures/ndtypeshttp://ndtypes.readthedocs.io/en/latest/https://pypi.python.org/pypi/ndtypes/0.2.0b1https://github.com/plures/xndhttp://xnd.readthedocs.io/en/latest/https://pypi.python.org/pypi/xnd/0.2.0b1
Checksums
=========
815455812cdeb0f8bd6c6d2111aca1c3ed23760d3c5a62dd45de20b37e01c0b1 ndtypes-0.2.0b1.tar.gz
fbfc4060fd95057d9882ae281b7d2b1b102283b8459ef5bfd8d4ffb21200d8df xnd-0.2.0b1.tar.gz
Stefan Krah
We are pleased to announce `homography`, a package for representation and
manipulation of 2D projective transformations (a construct which is commonly
used for image processing and computer graphics).
Supported on Python>=3.4.
Suggestions and contributions are welcome.
Installation: `pip install homography`
Documentation: https://satellogic.github.io/homography/
Code & Issues: https://github.com/satellogic/homography
Regards,
Amit Aronovitch, on behalf of the satellogic-devs team